AI/ML for Fusion Summer School 2026#
The Open and Fair Fusion for Machine Learning Applications Project
(June 1-12, 2026)
This is the landing page of the AI4Fusion Summer School held at William & Mary during Summer 2026
🚀 REGISTRATION IS NOW CLOSED
🗓️ Jan 31, 2026
Important
Classes will be held in rooms 3343 at the Integrated Science Center 4, William & Mary (School of Computing, Data Sciences and Physics).
Room 3346 will be used as lounge for students.
The conference room 3376 will be used by our speakers for their meetings.
For any question, please contact: wmsummerschool@gmail.com
Synopsis: An intensive 2-week summer school focused on undergraduate students with backgrounds in physics, engineering, computer science, applied mathematics and data science will be offered at William & Mary. This summer course will include a close to equal distribution of traditional instruction and active projects. The traditional instruction will provide daily 80 min instruction in morning classes with a focus on computing, applied mathematics, machine learning and fusion energy. These classes will be based on existing classes offered in data science at W&M, such as databases, applied machine learning and deep learning, Bayesian reasoning in data science. These classes will be supplemented with a class focused on fusion energy for the applications the students will tackle during the hands-on component and for students’ summer research. A draft agenda to be posted soon.
This course is based on the following references: [BGC17, KK22, RLM22]
📚 Past Editions#
⚠️ Note: Course material will appear below.
Schedule (June 1-12, 2026)
[Pre-flight]
📚 Data Science Lectures and 💻 Tutorials (ongoing)
- Lecture 1: Linear Models & Basic Recap of Matrix Operations
- Lecture 1/Tutorial: Ordinary Least Squares
- Lecture 2: Linear Classifiers
- Lecture 2/ Tutorial: Linear Classifiers Perceptron
- Lecture 3: Introduction to Deep Learning
- Lecture 4: Introduction to Deep Learning: Network Optimization
- Lecture 5: Decision Trees and XGBoost
- Lecture5/Tutorial: Decision Trees
- Lecture 6: Pytorch
- Lecture6/Tutorial: Pytorch Basics
- Lecture 6/Tutorial: Classification with Fusion Data
- Lecture 7: Convolutional Neural Networks
- Lecture 7/Tutorial: Convolutional Neural Networks
- Lecture 8: Graph Neural Networks
- Lecture 9: Generative AI (Part I)
- Lecture 10: Lecture 9: Generative AI (Part II)
- Lecture 11: Introduction to Transformers
- Lecture 13: Introduction to Vision Transformers (Part I)
- Lecture 14/Tutorial: Vision Transformer
- Lecture 15: Introduction to Bayesian Statistics
- Lecture 15/Tutorial: Bayes Nets
- Lecture 15/Tutorial: Markov Chain Monte Carlo
- Lecture 16: From Bayesian Regression to Gaussian Processes
- Lecture 17: Bayesian Neural Networks
- Lecture 17/Tutorial: Basic Bayesian Neural Network
👤💻🚀 Hands-On Sessions
📚 Fusion Lectures (being updated during the summer school)
- Introduction to Fusion (S. Mordijck)
- Tokamak Operations: Pegasus (S. Diem)
- Alcator C-Mod: Database Intro (A.R. Saperstein)
- Plasma Diagnostics (E. Kostadinova)
- Disruption Physics (A.R. Saperstein)
- Edge Localized Modes (S. Mordijck)
- Making Plasma Science Open (N. Murphy)
- Managing Data (N. Cummings)
- ML Uncertainty Quantification for the Experimental Fusion (C. Cowley)
Additional resources
Credits: Material on git, VS-Code, and HPC from AID2E